Modern process control is becoming increasingly computerized, and part of this development is the addition of electronic intelligence to process components. Valves, sensors, controllers and similar devices are broadcasting information back to the electronic controllers which at times are inundated with information. Only portions of this information are relevant to plant operation. Other information are either noise, or redundant.
Trends are a common feature in many applications used in industrial automation domain. They are used by operator of the process control to analyze what has happened to process variable with respect to time. Many times the trend is seen as a performance bottleneck. The trend is supposed to load large amounts of data for analysis. When an operator is attempting to detect long or medium-term data trends, it is very common to compress the data on the graph by packing the data points very close together without combining data points.
Data compression analyzes the signal of the data stream to extract the critical aspects from the process data. Most of the time, the data compression techniques compress the data without requiring knowledge about how the data is rendered. An example of such a technique is ‘sending one tuple for continuous range where the tag has maintained constant data’. However, this technique has serious limitations since most of the time the user is interested in data that has some dynamics associated with it. Also the technique does not take care of the tags with noise components.
Ideally this data compression should results in the more important points being kept. The data compression should benefit the operator by allowing a clearer display, uncluttered by noise. The process should aid interpretation by preserving significant aspects of the data, and showing clear trends.
Therefore, there exists a need for a device that resolves the above listed deficiencies. More particularly, the device needs to preserve the valuable aspects of the trend of a process control plant while reducing size of the trend.